High-Throughput Computational Exploration of MOFs with Open Cu Sites for Adsorptive Separation of Hydrogen Isotopes

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

Proton penetration mechanism and selective hydrogen isotope separation through two-dimensional biphenylene

Hydrogen isotope separation is of prime significance in various scientific and industrial applications. Nevertheless, the existing technologies are often expensive and energy demanding. Two-dimensional carbon materials are regarded as promising candidates for cost-effective separation of different hydrogen isotopes. Herein, based on theoretical calculations, we have systematically investigated the proton penetration mechanism and the associated isotope separation behavior through two-dimensional biphenylene, a novel graphene allotrope. The unique non-uniform rings with different sizes in the biphenylene layer resemble the topological defects of graphene, serving as proton transmission channels. We found that a proton can readily pass through biphenylene with a low energy barrier in some specific patterns. Furthermore, large kinetic isotope effect ratios for proton-deuteron (13.58) and proton-triton (53.10) were observed in an aqueous environment. We thus conclude that biphenylene would be a potential carbon material used for hydrogen isotope separation. This subtle exploitation of the natural structural specificity of biphenylene provides new insight into the search for materials for hydrogen isotope separation.

Accelerated screening and assembly of promising MOFs with open Cu sites for isobutene/isobutane separation using a data-driven approach

As the by-products of catalytic cracking or alkane dehydrogenation, isobutene (2-methyl-propylene) and isobutane (2-methyl-propane) are important chemical feedstocks, but the separation of their mixture is a challenging issue in the petrochemical industry. Herein, we report the first example of large-scale computational screening of metal-organic frameworks (MOFs) with copper open metal sites (Cu-OMS) on the adsorptive separation of isobutene/isobutane using configuration-bias Monte Carlo (CBMC) simulations and machine learning among >330 000 MOFs data. We discovered that the optimal structural features governing the MOFs-based separation of isobutene/isobutane were density (ρ) and porosity (φ), with ranges of 0.2-0.5 g cm^-3 and 0.8-0.9, respectively. Furthermore, the key genes (metal nodes or linkers of frameworks) contributing to such adsorptive separation were data-mined by feature engineering of ML. These genes were cross-assembled into novel frameworks using a material-genomics strategy. The screened AVAKEP, XAHPON, HUNCIE, Cu₂O₈-mof177-TDPAT_No730 and assembled Cu₂O₈-BTC_B-core-4_No1 possessed high isobutene uptake and isobutene/isobutane selectivity of >19.5 mmol g^-1 and 4.7, with high thermal stability (as validated by molecular-dynamics simulations) overcoming the critical "trade-off" problem to some extent. The macroporous structures (pore-limiting diameter >12 Å) of these five promising frameworks with multi-layer adsorption on isobutene resulted in high isobutene loading, as validated by adsorption isotherms and CBMC simulations. The higher adsorption energy and heat of adsorption of isobutene than those of isobutane indicated that the thermodynamic equilibrium drove their selective adsorption. Generalized charge decomposition analysis and localized orbit locator calculations based on density functional theory wavefunctions suggested that high selectivity was due to complexation of feedback π bonds between isobutene and Cu-OMS, but also the strong π-π stacking interaction induced by the CC bond of isobutene with the multiple aromatic rings and unsaturated bonds of frameworks. Our theoretical results and data-driven approach may provide insights into the development of efficient MOF materials for the separation of isobutene/isobutane and other mixtures.

Enhance Hydrogen Isotopes Separation by Alkali Earth Metal Dopant in Metal-Organic Framework

Kinetic quantum sieving (KQS) based on pore size and chemical affinity quantum sieving (CAQS) based on adsorption site are two routes of porous materials to separate hydrogen isotope mixtures. Alkali earth metals (Be, Mg, and Ca) were doped into UiO-67 to explore whether these metal sites can promote H₂/D₂ separation. Based on the zero-point energy and adsorption enthalpy calculated by density functional theory calculations, the Be dopant shows better H₂/D₂ separation performance than other alkali earth metal dopants and unsaturated metal sites in metal-organic frameworks based on CAQS. Orbital interaction strongly relates to the chemical affinity and further influences the D₂/H₂ selectivity. Moreover, the predicted D₂/H₂ selectivity of Be-doped sites (49.4) at 77 K is even larger than the best experimental result (26). Finally, the different dynamic behaviors of H₂ and D₂ on Be-doped UiO-67 indicate its strong H₂/D₂ separation performance via KQS.

Probing the Reactions of Thiourea (CH4N2S) with Metals (X = Au, Hf, Hg, Ir, Os, W, Pt, and Re) Anchored on Fullerene Surfaces (C59X)

Upon various investigations conducted in search for a nanosensor material with the best sensing performance, the need to explore these materials cannot be overemphasized as materials associated with best sensing attributes are of vast interest to researchers. Hence, there is a need to investigate the adsorption performances of various metal-doped fullerene surfaces: C₅₉Au, C₅₉Hf, C₅₉Hg, C₅₉Ir, C₅₉Os, C₅₉Pt, C₅₉Re, and C₅₉W on thiourea [SC(NH₂)₂] molecule using first-principles density functional theory computation. Comparative adsorption study has been carried out on various adsorption models of four functionals, M06-2X, M062X-D3, PBE0-D3, and ωB97XD, and two double-hybrid (DH) functionals, DSDPBEP86 and PBE0DH, as reference at Gen/def2svp/LanL2DZ. The visual study of weak interactions such as quantum theory of atoms in molecule analysis and noncovalent interaction analysis has been invoked to ascertain these results, and hence we arrived at a conclusive scientific report. In all cases, the weak adsorption observed is best described as physisorption phenomena, and CH₄N₂S@C₅₉Pt complex exhibits better sensing attributes than its studied counterparts in the interactions between thiourea molecule and transition metal-doped fullerene surfaces. Also, in the comparative adsorption study, DH density functionals show better performance in estimating the adsorption energies due to their reduced mean absolute deviation (MAD) and root-mean-square deviation (RMSD) values of (MAD = 1.0305, RMSD = 1.6277) and (MAD = 0.9965, RMSD = 1.6101) in DSDPBEP86 and PBE0DH, respectively.